AUTHOR=Wang Jia , Xiong Xing , Ye Jing , Yang Yang , He Jie , Liu Juan , Yin Yi-Li 

TITLE=A Radiomics Nomogram for Classifying Hematoma Entities in Acute Spontaneous Intracerebral Hemorrhage on Non-contrast-Enhanced Computed Tomography

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 16 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.837041

DOI=10.3389/fnins.2022.837041

ISSN=1662-453X

ABSTRACT=AIM
To develop and validate a radiomics nomogram on non-contrast-enhanced computed tomography (NECT) for classifying hematoma entities in patients with acute spontaneous intracerebral hemorrhage(ICH).
MATERIALS AND METHODS
One hundred and thirty-five patients with acute intraparenchymal hematomas and baseline NECT scans were retrospectively analyzed, including 52 patients with vascular malformations-related hemorrhage (VMH) and 83 patients with primary intracerebral hemorrhage (PICH). The patients were divided into training and validation cohorts in a 7:3 ratio with random seed. After extracting the radiomics features of hematomas from baseline NECT, least absolute shrinkage and selection operator (LASSO) regression was applied to select features and construct radiomics signature. Multivariate logistic regression analysis was used to determine the independent clinical-radiological risk factors, and a clinical model was constructed. Predictive radiomics nomogram was generated by incorporating radiomics signature and clinical-radiological risk factors. Nomogram performance was assessed in the training cohort and tested in the validation cohort. Capability of models were compared by calibration, discrimination, and clinical benefit.
RESULTS
Six features were selected to establish radiomics signature via LASSO regression. The clinical model was constructed with the combination of age (odds ratio [OR]: 6.731；95% confidence interval [CI]: 2.209-20.508) and hemorrhage location (OR: 0.089; 95% CI: 0.028-0.281). Radiomics nomogram (area under the curve [AUC], 0.912 and 0.919) that incorporated age, location and radiomics signature outperformed the clinical model (AUC, 0.816 and 0.779) and signature (AUC, 0.857 and 0.810) in the training cohort and validation cohorts respectively. Good calibration and clinical benefit of nomogram were achieved in the training and validation cohorts.
CONCLUSIONS:
NECT-based radiomics nomogram can predict the individualized risk of VMH in patients with acute ICH.